## COURSES

## INTRODUCTION TO LINEAR MODELS

### General Information

**Aim:**

This course is predominantly an applied statistical course, with emphasis on statistical theory only when needed. It aims to provide the basic theoretical and operational concepts to the student about Linear Econometric Models of cross-section data. The course will cover estimation and inference principles, the mathematical (algebraic properties) of the Ordinary Least Square methods, simple and multiple linear regression models, tests for functional form and omitted variables, in addition to heteroskedasticity and weighted least squares. It will also emphasize the nature of residuals and analyze many of the inspection and tests of goodness-of-fit and influential measures by means of residuals. The empirical part of the course will be based on the R software and data from Wooldridge (2016). I expect that students read the suggested literature specific to linear econometrics, including the basic texts on mathematical econometrics, probability, and statistical inference, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate cross-section data in R and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies.

**Tests and Grading:**

- Assignment 1: Estimation of a simple linear regression via OLS using Excel (20 points) [Download]
- Assignment 2: Applied use of cross-section data to estimate, interpret and analyze the quality of the model (30 points)
- Final test: a formal test covering the content of the course (50 points)

**Tutoring:**

Teaching Assistants:

*To be updated* (Doctor Student in Demography)

Tutoring hours: Thursday, 11:00 am to 12:30 pm (*to be updated*)

**More details: **

Download the complete syllabus here. Download

### Data & Scripts

#### Data

Datasets for Wooldridge Book (5th Edition) by Chapter on Cengage Website.

Datasets for Assignment 1.

#### Scripts

Class 1 – Simulation Probability Distributions in R.

Class 2 – Solution to the Computer Exercises (Chapter 1 – Wooldridge).

Class 3 – How to reproduce examples throughout the chapter (Chapter 2 – Wooldridge).

Class 4 – How to reproduce examples throughout the chapter (Chapter 3 – Wooldridge).

Class 4 – Solution to Computer Exercises (Chapter 2 – Wooldridge).

Class 4 – Frisch-Waugh-Lovell and Orthogonal Partitioned Regression Theorems (Simulation).

Class 5 – How to reproduce examples throughout the chapter (Chapter 4 – Wooldridge).

Class 5 – Solution to Computer Exercises (Chapter 3 – Wooldridge).

Class 5 – Central Limit Theorem and the Law of Large Numbers for convergence (Simulation).

Class 6 – How to reproduce examples throughout the chapter (Chapter 6 – Wooldridge).

Class 6 – Solution to Computer Exercises (Chapter 4 – Wooldridge).

Class 7 – How to reproduce examples throughout the chapter (Chapter 7 – Wooldridge).

Class 7 – Solution to Computer Exercises (Chapter 6 – Wooldridge).

Class 8 – How to reproduce examples throughout the chapter (Chapter 8 – Wooldridge).

Class 8 – Solution to Computer Exercises (Chapter 7 – Wooldridge).

Class 9 – Solution to Computer Exercises (Chapter 8 – Wooldridge).

### Writing Materials, Powerpoints & Beamers

### Compulsory Reading (Textbook)

Chapter 1 – The nature of econometrics and economic data.

Chapter 2 – The simple regression model.

Chapter 3 – Multiple Regression Analysis: Estimation.

Chapter 4 – Multiple Regression Analysis: Inference.

Chapter 6 – Multiple Regression Analysis:Further Issues.

Chapter 7 – Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables.

Chapter 8 – Heteroskedasticity.

### Weekly Assignments

Chapter 1 – Problems and Computer Exercises.

Chapter 2 – Problems and Computer Exercises.

Chapter 3 – Problems and Computer Exercises.

Chapter 4 – Problems and Computer Exercises.

Chapter 6 – Problems and Computer Exercises.

Chapter 7 – Problems and Computer Exercises.

Chapter 8 – Problems and Computer Exercises.

Chapter 9 – Problems and Computer Exercises.

Chapter 15 – Problems and Computer Exercises.

### Teaching Assistant's Material

### Video Classes

### Extra Materials

How to accommodate nonlinearities in LRM (variables transformation).

Confidence Interval using Profile Likelihood (profile-likelihood-ci).

Student’s material for Wooldridge’s book.

Fundamentals of Mathematical Statistics.

### References

#### Textbooks

Jeffrey M. Wooldridge *Introductory Econometrics: A Modern Approach*, 6th Edition, CENGAGE Learning, 2012.

Florian Heiss *Using R for Introductory Econometrics*, 1st Edition, Published using the independent publishing platform CreateSpace, 2016.

#### Same Level Reference Books

Kennedy, P. *A Guide to Econometrics*, Sixth Edition John Wiley & Sons, 2008.

Baum, C. *An Introduction to Modern Econometrics Using Stata*, Stata Press 2006.

Stock, J.H and M. W. Watson *Introduction to Econometrics*, 2nd ed., Addison-Wesley, 2006.

Hill, R. Carter, Griffths, William E. and Lim, Guay C. *Principles of Econometrics, *3rd ed., John Wiley & Sons, 2008.

#### Advanced Readings

Goldberger, A. S. *A Course in Econometrics **1st US Edition 4th Printing Edition*, Harvard University Press, 2000.

Greene,W.H. *Econometric Analysis*, Seventh Edition, Pearson/Prentice Hall, 2012.

Woodridge, J. *Econometric Analysis of Cross Section and Panel* *Data*, 2nd Edition,* *MIT Press, 2010.

Long, S. and J. Freese *Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences)*, 1nd Edition, Sage Publications, 1997.

Badi H. Baltagi *Econometric Analysis of Panel Data*, 4th Edition, Wiley, 2008.

James W. Hardin, Joseph M. Hilbe *Generalized Linear Models and Extensions*, 2nd Edition Stata Press, 2006.

Colin Cameron, Pravin K. Trivedi *Regression Analysis of Count Data,* Cambridge University Press, 1998.

John P. Hoffmann *Generalized Linear Models: An Applied Approach,* Pearson, 2004.

Cheng Hsiao *Analysis of Panel Data*, 2nd Edition, Cambridge University Press, 2003.